Predicting the Food-Energy Nexus of Wild Food Systems: Informing Energy Transitions for Isolated Indigenous Communities

Many isolated and indigenous Arctic and Subarctic communities are mixed-subsistence-cash economies. In the 20th century, the integration of motorized transport expanded access to harvestable resources but created energy dependence intricately linking food with energy security. As remote communities...

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Bibliographic Details
Main Authors: Schwoerer, Tobias, Schmidt, Jennifer I., Holen, Davin
Format: Article in Journal/Newspaper
Language:unknown
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Online Access:http://www.sciencedirect.com/science/article/pii/S0921800919321123
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Summary:Many isolated and indigenous Arctic and Subarctic communities are mixed-subsistence-cash economies. In the 20th century, the integration of motorized transport expanded access to harvestable resources but created energy dependence intricately linking food with energy security. As remote communities became more vulnerable to fossil fuel price fluctuations, they also face new challenges related to climate change such as shorter winter seasons. The transitioning from fossil fuels to local renewable energy sources is widely considered both an adaptation and mitigation strategy. We empirically analyzed the food-energy nexus for nine Arctic and Subarctic communities using a Bayesian hierarchical model. We predict household-level gasoline consumption based on wild food harvest and vehicle type. We found that cars and trucks are the least energy efficient forms of transportation whereas snowmobiles are the most efficient. Also, communities with strong traditional wild food production and specialization are more likely to be energy efficient. The results suggest that indigenous culture is an important contributor to effective climate mitigation, energy, and food security. Our analysis supports community adaptation and planning for improved quantitative understanding of the food-energy nexus in rural Arctic communities. The study assists communities with energy transitions away from fossil fuels towards local renewable energy sources. Arctic; Bayesian modeling; Energy transition; Indigenous communities; Transportation; Wild food systems;